Just for scale, a 0.05 increase in Gini coefficient is 2.5 times the standard deviation observed in the data, for which the coefficient varied from 0.41 to 0.5 at the state level.

Why would income inequality affect readmissions?

A “compositional” explanation suggests that the poor health outcomes can be attributed to the increased rates of poverty typically found in highly unequal societies. A complementary “contextual” explanation posits that a high level of inequality has corrosive effects on society, independent of its relation to individual income. For example, large differences in income can result in spatial concentrations of poverty, leading to diminished levels of social cohesion and social capital. […]

A patient’s risk of readmission is probably influenced by factors such as the strength of their social networks and support systems and by their capacity for managing their own care, including obtaining follow-up care, adhering to complex medication regimens, or complying with other instructions after discharge. Prior research has suggested that income inequality results in reduced levels of social capital and social cohesion.

Interestingly, however, the authors found no effect of income inequality on 30-day mortality for the same condition cohorts.

With this paper, the views of Krumholz and Joynt seem to be converging.

An empirical relationship between inequality and health at the aggregate level (here state) can arise simply because of aggregation bias. Deaton, I think, made that point a long time ago. If the underlying relationship between income and health is concave, aggregating will yield a relationship between measures of variation (variance, gini, etc) and average health. I am not saying this is what is happening here. But caution is necessary. Now, why would this cause a relationship between readmission and gini and not with mortality is puzzling.

“Our results should be interpreted in the light of their limitations. Firstly, our analysis included only Medicare beneficiaries with three conditions. Age, diagnosis, and insurance status could modify the relation between inequality and outcome, and our findings should be generalized with caution. Secondly, our regression models controlled for several important variables that might serve as potential confounders of the association between inequality and health outcomes. However, the number of possible sources of unmeasured confounding is large, and our results may reflect residual bias. Additionally, some of the factors that we treated as confounders could, in fact, lie on the causal pathway between inequality and the outcomes we investigated. To the extent that a patient’s comorbidities—or the number of hospital beds or physicians in a community—represent such mediators, our analysis could have underestimated the actual effect of inequality on readmission.

Thirdly, like many prior studies, we measured levels of inequality and outcomes during the same period of time, and attempted to account for a lag between a person’s exposure to inequality and outcome. Fourthly, we estimated patient income and education on the basis of zip code, which could have led to considerable misclassification. Fifthly, we chose to measure and analyze the effects of inequality at the level of the state, which has been the most common unit of analysis in US studies; however, levels of inequality vary within states, and alternative levels of geographic aggregation could have led us to different conclusions”

– If two states have precisely the same number of people, and precisely the same income distribution amongst workers, they can have vastly different gini-coefficients if the unit of measurement is households (which it generally is) if the population in one state has a higher tendency for those earning incomes to share a home.In a state with a population composed of two married couples in which each person earns precisely the same income, household income inequality will increase dramatically.

Without a more detailed analysis it’s difficult to determine whether the state-level association between outcomes and household income inequality documented in the study is primarily driven by a greater variance in earned incomes within a given state vs behavioral (fewer marriages/more divorce, etc) or demographic (higher percentage of young unmarrieds, etc) characteristics.